Following implementation details in Deformable DETR [1]}, we use ImageNet pre-trained ResNet-50 [2]} as our backbone CNN and extract multi-scale feature maps without FPN.
The number of attention heads and sampling offsets for deformable attentions are set to \(M=8\)  and \(K=4\) , respectively.
The AdamW optimizer is used with the initial learning rate of 2e-4 and weight decay of 1e-4.
All transformer weights are initialized with weights pre-trained in MS-COCO.
For a fair comparison with QPIC [3]}, we use only 100 HOI queries instead of using 300 ones as in Deformable DETR [1]}.
